import tensorflow.examples.tutorials.mnist.input_data as input_data
import tensorflow as tf

# 使用卷积神经网络进行MNIST


def weight_variable(shape):
    """
    创建权重
    :param shape:
    :return:
    """
    initial = tf.truncated_normal(shape, stddev=0.1)  # 标准差为0.1的正态分布
    return tf.Variable(initial)


def bias_variable(shape):
    """
    加入噪声，防止“死点”
    :param shape:
    :return:
    """
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def conv2d(x, w):
    """

    :param x:
    :param w:
    :return:
    """
    return tf.nn.conv2d(
        x,
        w,
        strides=[1, 1, 1, 1],
        padding='SAME'
    )


def max_pool_2x2(x):
    """

    :param x:
    :return:
    """
    return tf.nn.max_pool(
        x,
        ksize=[1, 2, 2, 1],
        strides=[1, 2, 2, 1],
        padding='SAME'
    )

# MNIST数据
mnist = input_data.read_data_sets('data_mnist', one_hot=True)

# 初始化计算图
sess = tf.InteractiveSession()

# 第一层卷积
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x = tf.placeholder("float", shape=[None, 784])
x_image = tf.reshape(x, [-1, 28, 28, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 第二层卷积
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 密集连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


# 输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 训练和评估模型
y_ = tf.placeholder("float", shape=[None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 计算图的变量初始化
sess.run(tf.global_variables_initializer())
for i in range(5000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(
            feed_dict={
                x: batch[0],
                y_: batch[1],
                keep_prob: 1.0
            }
        )
        print("step %d, training accuracy %g" % (i, train_accuracy))
    train_step.run(
        feed_dict={
            x: batch[0],
            y_: batch[1],
            keep_prob: 0.5
        }
    )

print("test accuracy %g" % accuracy.eval(
        feed_dict={
            x: mnist.test.images,
            y_: mnist.test.labels,
            keep_prob: 1.0
        }
    )
)

sess.close()
